Seam thickness is one of the most important parameters for reserve estimation of a lignite deposit. This paper addresses a case study on fuzzy estimation of lignite seam thickness from spatial coordinates. From the relationships between input (Cartesian coordinates) and output (thickness) parameters, fuzzy clustering and a fuzzy rule-based inference system were designed. Data-driven fuzzy model parameters were derived from numerical values directly. In addition, estimations of the fuzzy model were compared with kriging estimations. It was concluded that the performance of the fuzzy model was more satisfactory. The results indicated that the fuzzy modeling approach is very reliable for the estimation of lignite reserves.
Evaluating the geological properties of a mineral deposit is a fundamental task for mine planning and it requires an assessment of reserve parameters such as thickness and grade. This paper presents a linguistic model for estimating bauxite thickness within a deposit in a fuzzy environment using indicator geostatistics and fuzzy modeling. The proposed model consists of two main stages: determining the orebody boundary and estimating the thickness. In order to estimate the thickness, a rule-based fuzzy inference mechanism has been developed based on data statistics. Results and performance of the model have been compared with that of a well-known conventional technique, geostatistics (kriging), and it is shown that the proposed model has bigger estimation power. In addition, the fuzzy approach is more fl exible than the kriging approach. The fuzzy methodology used in the present paper is convenient for modeling reserve parameters.
The planning stages of mining require comprehensive and detailed analyses. The proper determination of the orebody boundary is one of the most important points to provide optimum model structure and projections. The limits can be determined by different methods based on the site geology. Although some three dimensional (3D) models were proposed for providing detailed information concerning a mine deposit, developing a solid model via a 3D approach is novelty. In other words, surface modeling should be performed for creating a solid model and determining limits of the deposit. In this way, sensible generation of the surface model can be achieved. This study investigated the estimation capability of the polynomial approach, which is a novel spatial interpolation method, for modeling a coal deposit surface. The performance of the proposed model was compared with some conventional methods in the literature. The results showed that the polynomial interpolation method is an effective method to employ for surface modeling of a mine deposit.
Factors affecting the final surface quality of polished marble are not yet fully understood. Clarifying these factors for optimization of multivariate polishing process by trial and error method is difficult, time-consuming, and costly task. In this study, the empirical practices were carried out using an experimental design, specifically, a central composite inscribed (CCI) design. The factors considered in CCI design were belt speed, rotational speed, and pressure of the polishing head, and the responses were surface glossiness and roughness. Mathematical models describing responses were produced using experimental datasets, and analysis of variance (ANOVA) was used to assess the fit of the models generated with the experimental data. For process optimization, desirability function analysis (DFA) was used. This study has shown that the CCI could efficiently be applied for the modelling of polishing machine for surface quality of marble strips. Better surface quality generally resulted from lower belt speeds, which increased contact time between the abrasives and strips. Optimized surface quality for marble specimen was established.
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